In various types of mechanical equipment, such as construction machines, medical devices, wind, solar, thermal and other kinds of power stations, and water treatment equipment, and plants, regular maintenance is carried out to prevent in advance a decreased operating rate caused by faults of mechanical equipment, or adverse effects on customers, such as unachieved final specifications due to deteriorated performance or quality, and lack of reliability. However, even through mechanical equipment is maintained regularly, the mechanical equipment unavoidably goes down or deteriorates in performance due to failures. For this reason, early detection of faults (fault sign detection) and early identification of fault parts (fault diagnosis) using data from sensors attached to mechanical equipment are important concepts as a matter of course, and monitoring (observing) the performance and quality using the above data is becoming an increasingly important concept.
However, from a large variety of sensor data and huge volumes of mechanical equipment information and maintenance history information, it is a challenging task to predict how long the mechanical equipment will operate without failures and what level of quality the mechanical equipment can keep (the remaining useful life of the equipment) by identifying the health state of the mechanical equipment and by further monitoring the performance and quality thereof. This is because such prediction requires both design knowledge and field knowledge and large volumes of data analysis and entails high difficulties.
For example, Patent Literature 1 describes a fault detection method for detecting a fault of a plant or facility at an early stage, wherein the method comprises acquiring data from multiple sensors and detecting a fault of observation data on the basis of the similarity between data sets.
Meanwhile, Patent Literature 2 describes a fault detection method for detecting a fault of a plant or facility at an early stage, the method including: acquiring data from multiple sensors; modeling learning data substantially consisting of normal data; calculating a fault measure of acquired data using the modeled learning data; modeling the time-series behavior of the acquired data by linear prediction; calculating a prediction error from the model; and detecting the presence of a fault using both the fault measure and the prediction error.
Non-Patent Literature 1 has proposed a technique for evaluating the remaining useful life (RUL) of lithium-ion batteries. This technique employs the Gaussian process which is a nonlinear regression procedure (see, for example, Non-Patent Literature 4) or a particle method (see, for example, Non-Patent Literature 5). Since the deterioration mechanism of lithium-ion batteries can be expressed with a relatively simple physical model and the parameters of the model can be determined from sensor data, this technique can obtain the RUL without facing major obstacle.
Non-Patent Literature 2 has proposed a diagnosis method for a hard disk drive. This method employs a classic technique, such as Mahalanobis distance, instead of the Gaussian process because hard disk drives have more various deterioration mechanisms than lithium-ion batteries.
In Prognostics and Health Management (PHM), RUL calculation is considered important. Non-Patent Literature 3 has provided agent software of integrating different types of information related to the RUL for aircrafts or the like.